TidyTemplate
2021-06-09
TidyTuesday
Personal aim
My today’s goal is to have a look at plotly animations (and maybe even use them) and the r paletteR package to create pallettes extracted from an image of choice.
Load the weekly Data
Download the weekly data and make available in the tt object.
tt <- tt_load("2021-06-08")##
## Downloading file 1 of 2: `stocked.csv`
## Downloading file 2 of 2: `fishing.csv`stocked<- tt$stocked
fishing<- tt$fishingReadme
Take a look at the readme for the weekly data to get insight on the dataset. This includes a data dictionary, source, and a link to an article on the data.
ttGlimpse Data
Take an initial look at the format of the data available.
stocked %>%
summary()## SID YEAR MONTH DAY
## Min. : 1 Min. :1950 Min. : 1.000 Min. : 0.00
## 1st Qu.: 15377 1st Qu.:1989 1st Qu.: 4.000 1st Qu.: 8.00
## Median : 31096 Median :1999 Median : 5.000 Median :16.00
## Mean : 8522639 Mean :1998 Mean : 5.291 Mean :15.65
## 3rd Qu.:20132395 3rd Qu.:2008 3rd Qu.: 6.000 3rd Qu.:23.00
## Max. :20198457 Max. :2018 Max. :12.000 Max. :31.00
## NA's :11414 NA's :13233
## LAKE STATE_PROV SITE ST_SITE
## Length:56232 Length:56232 Length:56232 Length:56232
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE GRID STAT_DIST
## Mode:logical Mode :logical Min. : 0 Length:56232
## NA's:56232 FALSE:2 1st Qu.: 607 Class :character
## NA's :56230 Median : 905 Mode :character
## Mean :1057
## 3rd Qu.:1410
## Max. :2806
## NA's :3089
## LS_MGMT SPECIES STRAIN NO_STOCKED
## Length:56232 Length:56232 Length:56232 Min. : 0
## Class :character Class :character Class :character 1st Qu.: 7213
## Mode :character Mode :character Mode :character Median : 15000
## Mean : 35368
## 3rd Qu.: 34600
## Max. :16586200
## NA's :7
## YEAR_CLASS STAGE AGEMONTH MARK
## Min. : 0 Length:56232 Min. : 0.0 Length:56232
## 1st Qu.:1995 Class :character 1st Qu.: 10.0 Class :character
## Median :2003 Mode :character Median : 15.0 Mode :character
## Mean :2001 Mean : 13.6
## 3rd Qu.:2010 3rd Qu.: 16.0
## Max. :2018 Max. :180.0
## NA's :17384 NA's :29511
## MARK_EFF TAG_NO TAG_RET LENGTH
## Min. : 0.00 Mode:logical Min. : 0.00 Min. : 0.0
## 1st Qu.: 90.20 NA's:56232 1st Qu.: 95.20 1st Qu.: 129.0
## Median : 98.00 Median : 97.87 Median : 161.4
## Mean : 79.16 Mean : 95.15 Mean : 160.6
## 3rd Qu.: 99.40 3rd Qu.: 99.00 3rd Qu.: 185.5
## Max. :100.00 Max. :100.00 Max. :13780.0
## NA's :50721 NA's :53587 NA's :30652
## WEIGHT CONDITION LOT_CODE STOCK_METH
## Min. : 0.0 Min. :0.000 Length:56232 Length:56232
## 1st Qu.: 136.1 1st Qu.:0.000 Class :character Class :character
## Median : 453.6 Median :0.000 Mode :character Mode :character
## Mean : 678.6 Mean :0.835
## 3rd Qu.: 862.0 3rd Qu.:1.000
## Max. :50268.0 Max. :7.000
## NA's :13445 NA's :16868
## AGENCY VALIDATION NOTES
## Length:56232 Min. : 0.000 Length:56232
## Class :character 1st Qu.: 2.000 Class :character
## Mode :character Median : 5.000 Mode :character
## Mean : 5.798
## 3rd Qu.: 9.000
## Max. :10.000
## NA's :10281fishing %>%
summary()## year lake species grand_total
## Min. :1867 Length:65706 Length:65706 Min. : 0.0
## 1st Qu.:1922 Class :character Class :character 1st Qu.: 10.0
## Median :1958 Mode :character Mode :character Median : 107.6
## Mean :1954 Mean : 1411.9
## 3rd Qu.:1988 3rd Qu.: 964.5
## Max. :2015 Max. :48821.0
## NA's :31767
## comments region values
## Length:65706 Length:65706 Min. : -31
## Class :character Class :character 1st Qu.: 0
## Mode :character Mode :character Median : 17
## Mean : 514
## 3rd Qu.: 217
## Max. :48405
## NA's :21916# quite few missing values in both datasetsWrangle
Explore the data and process it into a nice format for plotting! Access each dataset by name by using a dollarsign after the tt object and then the name of the data set.
# remove missing data
# fishing %>%
# filter(year==1991, lake=='Erie', species=='American Eel') # checking if there are any comments
fishing <-fishing %>%
select(year, lake,species, grand_total) %>%
filter(year != is.na(year) & lake != is.na(lake) & species != is.na(species) & grand_total != is.na(grand_total))%>% unique()
top_species<- fishing %>% select(species, grand_total,year)%>%
group_by(year)%>%
arrange(desc(grand_total), .by_group = TRUE)%>%select(species) %>% filter(year ==1879)
my_fish <-unique(top_species$species)
summary_f <- fishing %>%
group_by(year)%>%
summarise(mean=mean(grand_total))%>%
kable()%>%
kable_styling()Visualize
Using your processed dataset, create your unique visualization.
my_data<- fishing %>%
filter(species == 'Carp' | species == 'Lake Trout' | species=='Lake Whitefish')
plot<-ggplot(data= my_data, aes(x=year, y= grand_total, color = species))+
geom_point(size=0.3)+
stat_smooth(size=1.7, se = FALSE)+
scale_color_paletteer_d('awtools::a_palette')+
ylab(label = "fish total")+
xlab(label='year')+
labs(title = 'Fish in the Great Lakes')+
bbc_style()Creating a palette from an image
- using
paletteRpackage
scream_palette<-create_palette(image_path ="scream.jpg",
number_of_colors =5,
type_of_variable = "categorical")
plot_scream<-ggplot(data= my_data, aes(x=year, y= grand_total, color = species))+
geom_point(size=0.3)+
stat_smooth(size=1.7, se = FALSE)+
scale_color_manual(values = c(scream_palette[1], scream_palette[4], scream_palette[5] ))+
ylab(label = "fish total")+
xlab(label='year')+
labs(title = 'Fish in the Great Lakes')+
bbc_style()
plot_scream
pretty cool
fig <- ggplotly(plot)
figsteps <- list(
list(args = list("marker.year", "red"),
label = "1880",
method = "restyle",
value = "1"
),
list(args = list("marker.year", "green"),
label = "1920",
method = "restyle",
value = "2"
),
list(args = list("marker.year", "blue"),
label = "1960",
method = "restyle",
value = "3"
),
list(args = list("marker.year", "yellow"),
label = "2000",
method = "restyle",
value = "4"
)
)
anim <- fig %>%
layout(title = "Fish",
sliders = list(
list(
active = 1,
currentvalue = list(prefix = "Year: "),
pad = list(t = 60),
steps = steps)))
animSave Image
Save your image for sharing. Be sure to use the #TidyTuesday hashtag in your post on twitter!
# This will save your most recent plot
ggsave(plot=plot,
filename = "My TidyTuesday Plot.png",
device = "png")
ggsave(plot=plot_scream,
filename = "My TidyTuesday Plot with paletter.png",
device = "png")